class_docstr: A replay buffer shard for multiagent setups.
  
  This buffer is meant to be run in parallel to distribute experiences
  across `num_shards` shards. Unlike simpler buffers, it holds a set of
  buffers - one for each policy ID.
docstr: Returns the number of items currently stored in this buffer.
docstr_tok: ["Returns","the","number","of","items","currently","stored","in","this","buffer","."]
code: def __len__(self) -> int:
      """Returns the number of items currently stored in this buffer."""
      return sum(
          len(buffer._storage)
          for buffer in self.replay_buffers.values()
      )
  
imports: import collections
  import logging
  from enum import Enum
  from typing import Any, Dict, Optional
  from ray.util.timer import _Timer
  from ray.rllib.policy.rnn_sequencing import timeslice_along_seq_lens_with_overlap
  from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import Deprecated
  from ray.rllib.utils.from_config import from_config
  from ray.rllib.utils.replay_buffers.replay_buffer import _ALL_POLICIES, ReplayBuffer, StorageUnit
  from ray.rllib.utils.typing import PolicyID, SampleBatchType
  from ray.util.annotations import DeveloperAPI
  from ray.util.debug import log_once
label_desc: Use image node_id 2 for calling the MultiAgentReplayBuffer obj's underlying member method code with example usage: obj.__len__() and returns: sum